Parameter redundancy and identifiability in hidden Markov models

Autor:	Diana J. Cole
Rok vydání:	2019
Předmět:	Statistics and Probability Computer science Numerical analysis Markov process Symbolic computation Unobservable symbols.namesake Redundancy (information theory) symbols Identifiability QA276 Time series Hidden Markov model Algorithm
Zdroj:	METRON. 77:105-118
ISSN:	2281-695X 0026-1424
DOI:	10.1007/s40300-019-00156-3
Popis:	Hidden Markov models are a flexible class of models that can be used to describe time series data which depends on an unobservable Markov process. As with any complex model, it is not always obvious whether all the parameters are identifiable, or if the model is parameter redundant; that is, the model can be reparameterised in terms of a smaller number of parameters. This paper considers different methods for detecting parameter redundancy and identifiability in hidden Markov models. We examine both numerical methods and methods that involve symbolic algebra. These symbolic methods require a unique representation of a model, known as an exhaustive summary. We provide an exhaustive summary for hidden Markov models and show how it can be used to investigate identifiability.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e9ec43a70a55121b2f6e092a0ea1fbb5 https://doi.org/10.1007/s40300-019-00156-3 Zobrazit plný text záznamu Full text from SpringerLink